Contact-rich manipulation tasks often exhibit a large sim-to-real gap. For instance, industrial assembly tasks frequently involve tight insertions where the clearance is less than 0.1 mm and can even be negative when dealing with a deformable receptacle. This narrow clearance leads to complex contact dynamics that are difficult to model accurately in simulation, making it challenging to transfer simulation-learned policies to real-world robots. In this paper, we propose a novel framework for robustly learning manipulation skills for real-world tasks using simulated data only. Our framework consists of two main components: the "Force Planner" and the "Gain Tuner". The Force Planner plans both the robot motion and desired contact force, while the Gain Tuner dynamically adjusts the compliance control gains to track the desired contact force during task execution. The key insight is that by dynamically adjusting the robot's compliance control gains during task execution, we can modulate contact force in the new environment, thereby generating trajectories similar to those trained in simulation and narrowing the sim-to-real gap. Experimental results show that our method, trained in simulation on a generic square peg-and-hole task, can generalize to a variety of real-world insertion tasks involving narrow and negative clearances, all without requiring any fine-tuning. Videos are available at https://dynamic-compliance.github.io.

翻译：接触丰富的操作任务通常存在显著的仿真到现实鸿沟。例如，工业装配任务常涉及间隙小于0.1毫米的紧密插接，在处理可变形受件时甚至可能出现负间隙。这种狭窄间隙导致复杂的接触动力学，难以在仿真中精确建模，使得将仿真学习策略迁移至现实机器人面临挑战。本文提出一种仅使用仿真数据即可稳健学习现实任务操作技能的新框架。该框架包含两大核心组件："力规划器"和"增益调谐器"。力规划器同时规划机器人运动与期望接触力，而增益调谐器则动态调整顺应控制增益以追踪任务执行过程中的期望接触力。关键洞见在于：通过动态调整机器人在任务执行期间的顺应控制增益，能够在新环境中调控接触力，从而生成与仿真训练轨迹相近的动作，缩小仿真到现实鸿沟。实验表明，该方法在通用方形插桩仿真任务中训练后，无需任何微调即可泛化至多种涉及窄间隙和负间隙的真实插接任务。视频演示见 https://dynamic-compliance.github.io。